Turn raw warehouse data into tested, documented analytics models using dbt and modern development practices. 1. Who can join Suitable for: SQL analysts, data engineers and analytics engineers Mandatory prerequisites: Strong SQL, Git basics and familiarity with cloud data warehouses 2. Duration 44 live hours • Approximately 8 weeks at 6 hours per week; actual timetable to be confirmed. 3. Mode Classroom - Thaiyur / Kelambakkam, OMR Chennai (subject to schedule) Live Online (subject to schedule)
4. Syllabus — five modules
Module 1: The Modern Data Stack: Data Warehouses (Snowflake/BigQuery) & Analytics Engineering Learning outcome: Distinguish cloud data warehouse and analytics engineering roles. Module 2: dbt (data build tool) Setup: Models, Sources & Jinja Templating Learning outcome: Configure dbt projects, sources, macros and Jinja templates. Module 3: Modular SQL Transformation: Staging, Intermediate & Mart Layers Learning outcome: Build staged and mart SQL transformations with clear lineage. Module 4: Automated Data Quality Testing, Documentation & Data Lineage Learning outcome: Add automated quality tests and generate documentation. Module 5: CI/CD Integration with GitHub Actions for Production Data Models Learning outcome: Run a basic CI validation workflow for changes. 5. Practical project Source capstone title: Build a Production dbt Transformation Project over a Snowflake Warehouse Student deliverable: A dbt repository containing staged sources, dimensional models, tests, documentation and a CI workflow against a demo warehouse.
6. What is included Live trainer-led sessions Practical assignments Sample code or templates, as applicable Project feedback Learning assessment Perizona course-completion certificate subject to assessment 7. Upcoming batch Start date: To be announced. Students can register interest; dates are shared only after confirmation.
